ATR-MIR spectroscopy as a tool to assist 'Tempranillo' clonal selection process: Geographical origin and year of harvest discrimination and oenological parameters prediction

Tenderness of PGI “Ternera de Navarra” Beef Samples Determined by FTIR-MIR Spectroscopy

Understanding meat quality attribute changes during ageing by using non-destructive techniques is an emergent pursuit in the agroindustry research field. Using beef certified samples from the protected geographical indication (PGI) “Ternera de Navarra”, the primary goal of this study was to use Fourier transform infrared spectroscopy on the middle infrared region (FTIR-MIR) as a tool for the examination of meat tenderness evolution throughout ageing. Samples of the longissimus dorsi muscle of twenty young bulls were aged for 4, 6, 11, or 18 days at 4 °C. Animal carcass classification and sample proximate analysis were performed to check sample homogeneity. Raw aged steaks were analyzed by FTIR-MIR spectroscopy (4000–400 cm⁻¹) to record the vibrational spectrum. Texture profile analysis was performed using a multiple compression test (compression rates of 20%, 80%, and 100%). Compression values were found to decrease notably between the fourth and sixth day of ageing for the three compression rates studied. This tendency continued until the 18th day for C20. For C80 and C100, there was not a clear change in the 11th and 18th days of the study. Regarding FTIR-MIR as a prediction method, it achieved an R² lower than 40%. Using principal component analysis (PCA) of the results, the whole spectrum fingerprint was used in the discrimination of the starting and final ageing days with correct maturing time classifications. Combining the PCA treatment together with the discriminant analysis of spectral data allowed us to differentiate the samples between the initial and the final ageing points, but it did not single out the intermediate points.

Fourier transform infrared spectroscopy in monitoring the wine production

The complexity of the wine matrix makes monitoring of the winemaking process from the grapes to the final product crucial for the wine industry. In this context, analytical methodologies that can combine good accuracy, robustness, high sample throughput, “green character”, and by preference real-time analysis, are on-demand to create high-quality vitivinicultural products. In the last years, Fourier-transform Infrared Spectroscopy (FTIR) combined with chemometric analysis has been evaluated in several studies as an effective analytical tool for the wine sector. Some applications of FTIR spectroscopy have been already accepted by the wine industry, mainly for the prediction of basic oenological parameters, using portable and non-portable instruments, but still many others are waiting to be thoroughly developed. This literature review aims to provide a critical synopsis of the most important studies assessing grape and wine quality and authenticity, and to identify possible gaps for further research, meeting the needs of the modern wine industry and the expectations of most demanding consumers. The FTIR studies were grouped according to the main sampling material used - 1) leaves, stems, and berries; 2) grape must and wine applications - along with a summary of the basic limitations and future perspectives of this analytical technique.

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.